How Data Scientists Are Helping Retailers Predict Purchases and Returns

Despite recent blows to the footwear industry, there’s ample reason to be hopeful with cutting-edge technology . At least that’s according to Suresh Acharya, chief scientist of JDA Software Group Inc.’s research and development team and supply chain innovation hub, JDA Labs.

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“We’re living in amazing times where new innovations coming out of research in these fields is capturing people’s imaginations. And those innovations will be realities in the not-too-distant future,” said Acharya. “Today, we’re creating machine-learningalgorithmsAn algorithm is a fixed set of instructions for a computer. It can be very simple like "as long as the incoming number is smaller than 10, print "Hello World!". It can also be very complicated such as the algorithms behind self-driving cars. to help retailers incorporate all these sources of data and solve new business challenges.”

He pointed to returns forecasting — and how data science can help — as one example. “Fifteen years ago, retailers probably saw around seven to 10 percent of items returned. Today, pure-play e-tailers are seeing up to 60 or 70 percent of items coming back. That’s a logistical and forecasting challenge that can’t be ignored — and it’s one area where data science can begin to provide solutions,” said Acharya.

“If we analyze not only the attributes of items being returned, but the how, when and where of the original sale, we can identify correlations between sales and returns. From there, it’s a matter of determining the probability of a certain item being returned in the future. Analytics can also identify the customer segments with the highest propensity to return,” Acharya explained. “In this case, retailers can use technology to move from relying on forecasting based only on what they can see, to taking into account patterns they could never recognize on their own.”

The possibilities don’t end there. He explained there are several more ways retailers can take advantage of new technologies, using machine learning and artificial intelligenceArtificial Intelligence knows many different definitions, but in general it can be defined as a machine completing complex tasks intelligently, meaning that it mirrors human intelligence and evolves with time. to improve their business processes.

“Many people use [the terms] AI and machine learning interchangeably, but they’re actually different,” Acharya noted. “Machine learning is part of a larger AI ecosystem that overlaps with other areas of data science. For example, predictive analyticsPredictive Analytics describes the process of analysing data with statistical algorithms and machine learning in order to make prediction about future events based on historical data. A simple application is the weather forecast, more complex cases involve the prediction of consumer's behavior. can give us insights about future results, while prescriptive analytics can tell us the decisions we need to make today so that we’re more likely to see results we want.”

Acharya continued that while retailers are already gathering plenty of data, all the information is meaningless until optimized and analyzed. […]